Methods to Address Confounding and Other Biases in Meta-Analyses: Review and Recommendations

Meta-analyses contribute critically to cumulative science, but they can produce misleading conclusions if their constituent primary studies are biased, for example by unmeasured confounding in nonrandomized studies. We provide practical guidance on how meta-analysts can address confounding and other biases that affect studies' internal validity, focusing primarily on sensitivity analyses that help quantify how biased the meta-analysis estimates might be. We review a number of sensitivity analysis methods to do so, especially recent developments that are straightforward to implement and interpret and that use somewhat less stringent statistical assumptions than do earlier methods. We give recommendations for how these newer methods could be applied in practice and illustrate using a previously published meta-analysis. Sensitivity analyses can provide informative quantitative summaries of evidence strength, and we suggest reporting them routinely in meta-analyses of potentially biased studies. This recommendation in no way diminishes the importance of defining study eligibility criteria that reduce bias and of characterizing studies' risks of bias qualitatively.

Automated summary

Meta-analyses play a critical role in cumulative science, but can lead to misleading conclusions if the primary studies they include are biased. This article provides practical guidance for addressing biases that affect the internal validity of studies in meta-analyses, focusing on sensitivity analyses to quantify potential biases. Various sensitivity analysis methods are reviewed, with a focus on recent developments that are easy to implement and interpret. The importance of routinely reporting sensitivity analyses in meta-analyses of potentially biased studies is emphasized.